/**
 * Team composer for auto-routing.
 *
 * Maps a TaskAssessment to a concrete TeamSpec using a routing table derived
 * from lifecycle eval data (s01–s04). The routing table is a pure data
 * structure — update it as eval pass rates change.
 *
 * Key invariants (empirically verified by eval suite):
 *   - Haiku is worker-only. It is never selected as lead.
 *   - Sonnet + one-liner onboarding = 100% lifecycle reliability → default lead.
 *   - Opus lead only for high complexity (capable but verbose/expensive).
 *   - Worker model matches subtask complexity, not overall task complexity.
 *   - For codex workers, prefer gpt-5.5 or gpt-5.4-mini. See CODEX_MODEL_TIERS.
 */
import type { TaskAssessment } from './classifier.js';
export type ModelTier = 'haiku' | 'sonnet' | 'opus';
export type OnboardingVariant = 'bare' | 'one-liner' | 'brief' | 'skill';
/**
 * Which CLI harness to use for an agent.
 * Extend as opencode model evals complete and confirm role fitness.
 */
export type WorkerCli = 'claude' | 'codex' | 'opencode' | 'gemini' | 'droid';
/**
 * Roles from the choosing-swarm-patterns skill.
 * Each role slot in a pattern is filled by a harness+model with confirmed fitness.
 */
export type AgentRole = 'lead' | 'coordinator' | 'worker' | 'planner' | 'reviewer' | 'critic' | 'verifier' | 'judge' | 'mapper' | 'reducer' | 'supervisor' | 'debater';
/**
 * Role fitness for a harness+model combination.
 * Derived from lifecycle eval pass rates (s01–s06).
 *
 * Fitness levels:
 *   'confirmed' — s03+s04 ≥5 full-repeat runs pass reliably
 *   'provisional' — passes but with caveats (phantom rate, onboarding dependency)
 *   'not-viable' — fails lifecycle or not relay-native
 *   'untested' — eval not yet run
 */
export type RoleFitness = 'confirmed' | 'provisional' | 'not-viable' | 'untested';
export interface RoleFitEntry {
    fitness: RoleFitness;
    notes?: string;
}
export interface HarnessRoleMap {
    harness: string;
    defaultModel?: string;
    roles: Partial<Record<AgentRole, RoleFitEntry>>;
    bestOnboarding: OnboardingVariant;
    relayNative: boolean;
}
export interface WorkerSpec {
    role: AgentRole | string;
    model: ModelTier;
    task: string;
    /** Override CLI harness. Defaults to 'claude'. */
    cli?: WorkerCli;
    /** For codex workers: the OpenAI model name to pass via --model. */
    codexModel?: string;
    /** For opencode workers: the opencode model suffix. */
    opencodeModel?: string;
}
export interface TeamSpec {
    lead: {
        model: ModelTier;
        onboarding: OnboardingVariant;
    };
    workers: WorkerSpec[];
}
export declare const CODEX_MODEL_TIERS: {
    /**
     * gpt-5.5 — best. 16/16 scenarios PASS, 100% s03 one-liner+, 100% s04 all.
     * Recommended default for codex workers.
     */
    readonly recommended: "gpt-5.5";
    /**
     * gpt-5.4-mini — viable budget tier. 15/16 scenarios PASS (only s03:skill fails).
     * 100% s03 bare/one-liner, 80% brief/skill. 80–100% s04. Use bare or one-liner
     * onboarding for best results. phantom=31% (slightly noisy).
     */
    readonly budget: "gpt-5.4-mini";
    /**
     * gpt-5.4 — avoid. 16/16 scenarios PASS on majority-vote but phantom=52% (14
     * phantom agents) and per-run reliability is 60% across s03/s04 variants.
     * The config migration alias `gpt-5.4 → gpt-5.5` does NOT apply at runtime —
     * these are distinct models with significantly different behaviour.
     */
    readonly avoid: "gpt-5.4";
    /**
     * gpt-5.3-codex-spark — not viable. 6/16 scenarios PASS. Fails s03 one-liner/
     * brief/skill, s04 one-liner/brief. Ultra-fast but sacrifices relay reliability.
     */
    readonly notViable: "gpt-5.3-codex-spark";
};
export declare const HARNESS_ONBOARDING: Record<string, OnboardingVariant>;
export declare const HARNESS_ROLE_MAP: HarnessRoleMap[];
/**
 * Look up which harnesses can fill a given role at 'confirmed' or better fitness.
 * Use to populate role slots when composing teams for specific swarm patterns.
 */
export declare function harnessesForRole(role: AgentRole): HarnessRoleMap[];
/**
 * Map a TaskAssessment to a TeamSpec using the routing table.
 * Pure function — no I/O, no LLM call.
 */
export declare function composeTeam(assessment: TaskAssessment, originalTask: string): TeamSpec;
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